Identifying infants at risk for sudden infant death syndrome

ABSTRACT

A method for identifying infants at risk for SIDS includes applying electrodes to an infant, receiving electrical signals from the electrodes, analyzing the received electrical signals to measure alternans of a heart of the infant, and identifying whether the infant is at risk for SIDS. A system for identifying infants at risk for SIDS includes an input unit configured to receive electrical signals from electrodes applied to an infant, a processor connected to the input unit and configured to process the received electrical signals to measure alternans of a heart of the infant, and a comparator configured to compare the measured alternans with alternans in a population of infants.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. application Ser.No. 10/173,307 filed Jun. 18, 2002 and titled “Identifying Infants atRisk for Sudden Infant Death Syndrome,” which is incorporated byreference in its entirety.

TECHNICAL FIELD

This disclosure is directed to the identification of infants at risk forthe Sudden Infant Death Syndrome.

BACKGROUND

The Sudden Infant Death Syndrome (SIDS) is a disorder in which infantssuddenly die, usually during sleep. For every one thousand infants,between approximately one and two die of SIDS, making SIDS the leadingcause of death after the neonatal period in the first year of life. SIDSis thus an enormous human tragedy that has devastating consequences forthe affected infants and their families.

SUMMARY

Improved identification of infants at risk for SIDS is provided bymeasurement of alternans, for example, T-wave alternans, of an infantheart. Alternans is a subtle beat-to-beat change in the repeatingpattern of an infant's or other patient's electrocardiogram (ECG)waveform. Alternans results in an ABABAB . . . pattern of variation ofwaveform shape between successive beats in an ECG waveform. The level ofvariation is indicative of the likelihood that an infant is at risk forSIDS.

A method for identifying infants at risk for Sudden Infant DeathSyndrome includes applying electrodes to an infant, receiving electricalsignals from the electrodes, analyzing the received electrical signalsto measure alternans of a heart of the infant, and identifying whetherthe infant is at risk for SIDS.

In another aspect, another method for identifying infants at risk forSudden Infant Death Syndrome includes applying electrodes to an infantpatient, receiving electrical signals from the electrodes, analyzing thereceived electrical signals to measure alternans of a heart of theinfant, and comparing the measured alternans with alternans in apopulation of infants.

These methods may include one or more of the following features. Forexample, the received electrical signals may be analyzed to measureT-wave alternans, and the measured T-wave alternans may be compared withT-wave alternans in a population of infants. The method may also includeelevating a heart rate of the infant.

Elevating the heart rate of the infant may include stressing the infant.The method may also include analyzing the received electrical signals tomeasure a QT interval of the heart of the infant. The method may alsoinclude comparing the measured QT interval with QT intervals in apopulation of infants. The received electrical signals may includeelectrocardiogram signals. Analyzing the received electrical signals mayinclude sampling the processed signal at a frequency less than or equalto twice a frequency corresponding to alternans.

Analyzing the received electrical signals may include using a spectralapproach to measure alternans. Using a spectral approach to measurealternans may include digitizing the waveform at a plurality of samplepoints for each cycle, and constructing two-dimensional sample pointmatrices, having rows and columns, from the digitized waveform. Using aspectral approach to measure alternans may also include analyzingvariability in each column of the sample point matrices to form an indexcorrelating with the physiologic stability. Using a spectral approach tomeasure alternans may also include computing the alternating energy ateach of the sample points for the series of beats, and summing thealternating energy over the entire set of sample points to generate atotal alternating energy.

Analyzing the received electrical signals may include using ananalytical approach to measure alternans. Using the analytical approachmay include processing the received electrical signals to create aprocessed signal having an asymmetric spectrum, and processing theprocessed signal to measure alternans in the received electricalsignals. Processing the received electrical signals to create aprocessed signal may include creating the processed signal as ananalytical signal. Creating the processed signal as an analytical signalmay include generating a frequency domain representation of the receivedelectrical signals, modifying the frequency domain representation toremove components corresponding to negative frequencies, and generatingthe analytical signal as a time domain representation of the modifiedfrequency domain representation. Processing the processed signal mayinclude processing samples of the processed signal spaced by intervalsgreater than or equal to half the period of alternans. Processing thereceived electrical signals may include creating an approximation of ananalytical signal version of the received electrical signals. Processingthe processed signal may include sampling the processed signal at afrequency less than or equal to twice a frequency corresponding toalternans.

A system for the identification of infants at risk for Sudden InfantDeath Syndrome includes an input unit configured to receive electricalsignals from electrodes applied to an infant, a processor connected tothe input unit and configured to process the received electrical signalsto measure alternans of a heart of the infant, and a comparatorconfigured to compare the measured alternans with alternans in apopulation of infants

This system may include one or more of the following features. Forexample, the processor may be configured to process the receivedelectrical signals to measure T-wave alternans of the heart of theinfant, and the comparator may be configured to compare the measuredT-wave alternans with T-wave alternans in a population of infants. Theprocessor may be configured to process the received electrical signalsto measure a QT interval of the heart of the infant. The comparator maybe configured to compare the measured QT interval with QT intervals in apopulation of infants. The received electrical signals may includeelectrocardiogram signals. The system may also include ananalog-to-digital converter configured to sample the received electricalsignals at a frequency less than or equal to twice a frequencycorresponding to alternans to generate sample points.

The processor may be configured to compute an alternating energy at eachof the sample points for the series of beats. The processor may includea matrix constructor configured to construct sample point matrices,having rows and columns, from the generated sample points, an adderconfigured to sum the alternating energy over the entire set of samplepoints to generate a total alternating energy, and a divider configuredto normalize the total alternating energy with respect to an energy ofthe average waveform. The processor may be configured to create aprocessed signal having an asymmetric spectrum, and to process theprocessed signal to generate an indication of alternans in the receivedelectrical signals.

The processor may be configured to create the processed signal as ananalytical signal. The processor may be configured to create theprocessed signal as an analytical signal by generating a frequencydomain representation of the received electrical signals, modifying thefrequency domain representation to remove components corresponding tonegative frequencies, and generating the analytical signal as a timedomain representation of the modified frequency domain representation.The input unit may include circuitry configured to receive anelectrocardiogram signal. The system may also include an electrodeconnected to the input unit and configured to produce anelectrocardiogram signal. The processor may be configured to sample theelectrocardiogram signal at a frequency of once per beat.

By measuring alternans of a heart of the infant, infants at risk forSIDS may be identified. This will help focus preventative measures onthe infants most likely to suffer from SIDS, and spare the infants andtheir families the suffering caused by SIDS.

Other features and advantages will be apparent from the followingdescription, including the drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a graph of an ECG waveform for a single beat.

FIGS. 2A and 2B are graphs of, respectively, a band-limited signal andthe power spectrum of the signal.

FIGS. 3A and 3B are graphs of, respectively, the band-limited signal ofFIG. 2A sampled at a frequency greater than twice the frequency of thehighest frequency component of the band-limited signal, and thecorresponding power spectrum for the sampled signal.

FIGS. 4A and 4B are graphs of, respectively, the band-limited signal ofFIG. 2A sampled at a frequency less than twice the frequency of thehighest frequency component of the band-limited signal, and thecorresponding power spectrum for the sampled signal.

FIG. 5 is a flow chart of a spectral approach for processing ECGsignals.

FIG. 6A is a plot of the heart rate of a patient versus time during amotion test; FIG. 6B is plot of the motion rate of the patient versustime; and FIG. 6C is a plot of the motion rate divided by the heart rate(solid line) and the first sub-harmonic of the stepping rate divided bythe heart rate (dotted line).

FIG. 7 is a flow chart of an analytical approach for processing ECGsignals.

FIGS. 8A and 8B are graphs of, respectively, a band-limited signal andthe power spectrum of the signal.

FIG. 9 is a graph of the transfer function of a filter used to generatean analytical signal from a band-limited signal.

FIG. 10 is a graph of a power spectrum of the analytical signal.

FIGS. 11A and 11B are graphs of, respectively, the analytical signalsampled at a frequency less than twice the frequency of the highestfrequency component of the band-limited signal, and the correspondingpower spectrum.

FIGS. 12A and 12B are graphs of power spectra generated using,respectively, an analytical signal approach and the spectral method.

FIG. 13 is a flow chart of a method for identifying infants at risk forSIDS.

DETAILED DESCRIPTION

Techniques are provided for using alternans measurements to identifyinfants at risk for SIDS. Before the techniques are discussed withrespect to FIG. 13, more general techniques for detecting and measuringalternans are discussed with respect to FIGS. 1-12B.

Referring to FIG. 1, an ECG waveform for a single beat is typicallyreferred to as a PQRST complex. Briefly, the P wave appears atinitiation of the beat and corresponds to activity in the atria, whilethe QRST complex follows the P wave and corresponds to ventricularactivity. The QRS component represents the electrical activation of theventricles, while the T wave represents their electrical recovery. TheST segment is a relatively quiescent period. The T wave interval of theECG complex can be monitored to detect alternans. That is, a level ofvariation in the T waves of alternating beats can indicate theelectrical stability of the ventricles—the heart's main pumpingchambers.

While an ECG waveform typically has a QRS amplitude measured inmillivolts, an alternans pattern of variation with an amplitude on theorder of a microvolt may be clinically significant. Accordingly, thealternans pattern may be too small to be detected by visual inspectionof the electrocardiogram and often must be detected and quantifiedelectronically. Such electronic detection and quantification of thealternans pattern is further complicated by the presence of noise in theECG waveforms, as the noise may result in beat-to-beat variations thathave a larger magnitude than the alternans pattern of variation.

The noise in an ECG signal can be classified into three categories:baseline noise generated in the electrode, physiologic noise, andexternal electrical noise. The baseline noise is low frequency noisethat appears as an undulating baseline upon which the ECG rides.Baseline noise is attributable to motion and deformation of theelectrode, and results from low frequency events such as infantrespiration and infant motion. Typically, the frequency content ofbaseline noise is below 2 Hz.

Physiologic noise results from physiologic processes, such as skeletalmuscle activity, that interfere with the ECG signal. The electricalactivity of the skeletal muscles creates potentials that are additivewith respect to the potentials created by the heart. The frequencycontent of the skeletal muscle signals is comparable to the frequencycontent of the QRS complex, and is typically greater than 10 Hz. Whenmeasuring T-wave alternans, additional physiologic noise may result fromchanges in the position of the heart due to respiration or from changesin the projection of the electrical potential from the heart to the skinsurface due to thoracic conductivity changes arising from the inflationand deflation of the lungs with respiration.

External electrical noise results, for example, from ambientelectromagnetic activity in the room, electrode cable motion, andvariations in amplifiers or other components of the ECG circuitry.External electrical noise may be eliminated or reduced through the useof high quality components and through the reduction of ambientelectromagnetic activity by, for example, deactivating high powerequipment.

The impact of noise upon alternans identification is magnified by thefact that the ABABAB . . . variation in alternating beats occurs atexactly one half the frequency of the beats themselves. By sampling theECG waveform only once per beat and then trying to determine thealternating beat to beat variation of the samples, the ECG signal cannotbe band limited to comply with the Nyquist requirement, as illustratedby FIGS. 2A-4B.

Theoretically, to avoid aliasing when sampling a signal at a given rate,F_(S), the signal must be band limited to half of the samplingfrequency, 0.5 F_(S), which is referred to as the Nyquist frequency.

FIGS. 2A and 2B show, respectively, a band-limited analog signalx_(a)(t) and the power spectrum X_(a)(f) for that signal. Note that thepower spectrum is symmetric about zero.

When the analog signal is sampled, the spectrum for the sampled signalis periodic with a period equal to the sampling frequency, F_(S). FIGS.3A and 3B show a case in which the sampling frequency is greater thantwice the signal bandwidth, 2B. As shown, there is no interferencebetween adjacent spectral periods, and, accordingly, an accuratemeasurement of signal power at all frequencies of the original analogsignal can be made by considering the spectrum for a spectral period.

FIGS. 4A and 4B show a case in which the sampling rate is smaller than2B. As shown, interference between adjacent spectral periods distortsthe spectrum for the frequencies of overlap.

As shown in FIGS. 4A and 4B, failure to comply with the Nyquistrequirement (i.e., use of a sampling frequency smaller than twice thesignal bandwidth) results in underestimation of signal power at alloverlapped frequencies including the Nyquist frequency. For alternansdetection, the sampling rate is limited to one sample per beat and,since the alternans frequency is at exactly the Nyquist frequency, thesignal cannot be band limited to comply with the Nyquist requirement.

Spectral Approach:

One alternans measurement approach that addresses the effects of noiseis a spectral approach for measuring T-wave alternans. This approach isdescribed in detail in U.S. Pat. No. 4,802,491, which is incorporatedherein by reference. In summary, referring to FIG. 5, a method 500involves collecting (step 510) using, for example, a high inputimpedance amplifier and an analog-to-digital converter, and concurrentlyanalyzing 128 beats of a continuous stream of ECG signals using, forexample, a programmable processor. The spectral approach usesmeasurements from time synchronized points of consecutive T waves. Atime series is created by measuring, for each of the 128 beats, theT-wave level at a fixed point relating to the QRS complex (step 520).This process is repeated to create a time series for each point in theT-wave. A frequency spectrum is then generated for each time series(step 530), and the spectra are averaged to form a composite T-wavealternans spectrum (step 540). Since the T-waves are sampled once perbeat for each time series, the spectral value at the Nyquist frequency,i.e. 0.5 cycle per beat, indicates the level of beat-to-beat alternationin the T-wave waveform.

The alternans power is calculated from the composite T-wave alternansspectrum (step 550) and statistically compared to the noise power todiscriminate the beat-to-beat T-wave variation due to abnormalelectrical activity of the heart from the random variation due tobackground noise (step 560). The alternans power is calculated bysubtracting the mean power in a reference band used to estimate thebackground noise level (for example, the frequency band of 0.44-0.49cycle per beat) from the power at the Nyquist frequency (0.50 cycle perbeat). Alternans may be considered to be significant if the alternansexceeds noise by a threshold amount. Alternans may be considered to besignificant if the alternans is at least three times the standarddeviation of the noise in the noise reference band.

The spectral approach for T-wave alternans measurement is accurate inthe case of T-wave alternans measured during well controlled motion at ⅓or ⅔ of the heart rate. This is because two conditions tend to reduce oreliminate the effects of failure to comply with the Nyquist requirement.

First, the noise within the noise band can be considered to be white.Since the spectrum for white noise is flat for all frequencies, there isinterference from multiple adjacent spectral cycles. This, in turn,means that interference due to noise is statistically equivalent for allfrequencies.

Second, as noted above, the alternans is phased-locked (i.e. the ECGsignal is sampled at synchronized points). This means that the signalsat the Nyquist frequency interfere with consistent phase, which resultsin a correct estimation of signal power at this frequency.

Analytical Approach:

Colored noise in the ECG waveform also can mimic the presence ofalternans where none exists. For example, if an infant is breathing atone half or one third of the heart rate, the respiration may introduce aharmonic signal having the ABABAB . . . pattern of alternans. Motionthat repeats with some periodicity can create electrode noise with asimilar pattern. In processing a signal that includes colored noise,errors may result if one assumes that the noise is white, and ananalytical approach should be used.

For example, artifacts due to infant respiration or due to repetitiveinfant motion like sucking may cause colored noise to occur in the noiseband at the alternans frequency. FIGS. 6A-6C show a typical case inwhich the rate of a infant's motion is close to the heart rate. FIG. 6Ashows the heart rate as a function of time, FIG. 6B shows the motionrate, and FIG. 6C shows the motion rate and its sub-harmonic, normalizedto the heart rate. In this particular case, the motion creates artifactsat frequencies close to half of the heart rate.

In a case such as is illustrated in FIGS. 6A-6C, since the noise withinthe noise band is colored, interference between components from adjacentspectra of different phase results in underestimation of noise andtherefore overestimation of alternans power, which in turn may producefalse positive results for T-wave alternans tests.

Referring to FIG. 7, problems associated with the presence of colorednoise may be avoided through use of an analytical signal technique 600.According to the technique 600, an ECG signal is processed using a 50 Hzfilter (step 605) and a 60 Hz filter (step 610). This processing reducesthe effects of line voltages used to power the equipment that generatesthe ECG signal, with 60 Hz being the standard line voltage frequency inthe U.S. and 50 Hz being standard in Europe.

Next, an analytical version of the signal is created (steps 615-635).First, the signal is low-pass filtered (step 615). In oneimplementation, the low pass filter is a 5^(th) order Butterworth filterwith a zero phase configuration. The filtered signal is then transferredto the frequency domain using a fast Fourier transform (FFT) (step 620).

In the frequency domain, the portions of the frequency spectrumcorresponding to negative frequencies are removed (step 625). Thetechnique then compensates for removal of negative frequencies bydoubling all positive, non-zero components of the frequency spectrum(step 630). An inverse fast Fourier transform (IFFT) is then performedon the modified frequency spectrum to produce an analytical signal inthe time domain (step 635).

Next, the analytical signal is referenced to an analytical version ofWilson's central terminal (step 640). Wilson's central terminal (WCT) isa well-known ECG reference value. The analytical version of WCT isgenerated from the standard WCT using the procedure set forth in steps615-635. The analytical signal is referenced to the analytical versionof WCT by determining the difference between the two signals.

The referenced analytical signal then is processed similarly to thespectral approach. In particular, the referenced analytical signal issampled at time synchronized points on the T wave for a collection of128 beats (step 645), and a time series is created for each point on thecollection of T waves (step 650). As in the spectral method, a timeseries is created by measuring, for each of the 128 beats, the T-wavelevel at a fixed point relative to the QRS complex. This process isrepeated to create a time series for each point in the T wave.

Next, the time series are processed to reduce noise such as thatresulting from baseline wander (step 653). In general, this processinguses other signals, including those corresponding to respiration andimpedance, to adaptively remove baseline wander. Techniques forprocessing the time series are described in more detail in U.S. Pat. No.5,704,365, titled “USING RELATED SIGNALS TO REDUCE ECG NOISE,” which isincorporated by reference.

A frequency spectrum is then generated for each time series (step 655),and the spectra are averaged to form a composite T-wave alternansspectrum (step 660). Since the T-waves are sampled once per beat foreach time series, the spectral value at the Nyquist frequency, i.e. 0.5cycle per beat, indicates the level of beat-to-beat alternation in theT-wave waveform.

Finally, the alternans power is statistically compared to the noisepower to discriminate the beat-to-beat T-wave variation due to abnormalelectrical activity of the heart from the random variation due tobackground noise (step 665). The alternans power is calculated bysubtracting the mean power in a reference band used to estimate thebackground noise level from the power at the Nyquist frequency (0.50cycle per beat). In one implementation, the reference band includesfrequencies from 0.43 to 0.49 and 0.51 to 0.56 cycles per beat. In thesame implementation, alternans is considered to be significant if it isat least three times the standard deviation of the noise in the noisereference band.

In general, the technique 600 reduces or eliminates the effects ofaliasing. The amount of aliasing depends on the infant's heart rate andreduces as the heart rate increases. For heart rates of primaryinterest, such as 95 to 175 beats per minute, the sampling frequency isapproximately 2.5 Hz. In the spectral method, this would have meant thatany signal component of frequency content over 1.25 Hz would be a sourceof aliasing.

Since aliasing is primarily due to the interference between thefrequency components at the positive part of the spectrum and those atthe negative part of the spectrum from an adjacent period of thespectrum, creation of an analytical signal serves to avoid aliasing. Inparticular, creation of the analytical signal removes the interferingnegative frequency components while scaling the signal to preserve thetotal signal energy.

An analytical signal is a complex signal. See Proakis J G, Manolakis DG, Digital Signal Processing, Prentice Hall, Upper Saddle River, N.J.,1996, pp. 738-742, which is incorporated by reference. The real part ofthe complex signal, y, is the original signal, x, and the imaginary partis the Hilbert transform, H(x), of the original signal:y=x+jH(x),where H(x) is the Hilbert Transform of x with the following transferfunction. ${H(\omega)} = \left\{ \begin{matrix}{- j} & {for} & {0 < \omega \leq {+ n}} \\{+ j} & {for} & {{- \pi} < \omega \leq 0}\end{matrix} \right.$

The Hilbert Transform returns a complex sequence. This sequence is aversion of the original real sequence with a 90° phase shift. It has thesame amplitude and frequency content as the original real data andincludes phase information that depends on the phase of the originaldata.

The overall transform has the following real transfer function:${H_{a}(\omega)} = \left\{ \begin{matrix}0 & {for} & {{- \pi} < \omega \leq 0} \\1 & {for} & {\omega = 0} \\2 & {for} & {0 < \omega \leq {+ \pi}}\end{matrix} \right.$

The analytic signal is characterized as having an asymmetric spectrumwith components of negative frequency having been removed. A variety oftime domain and frequency domain processing methods and filters that canbe used to implement or approximate the analytic signal approach. Thesemethods affect certain frequencies ω_(n) of the input signal differentlyfor the positive frequency +|ω_(n)| and the corresponding negativefrequency −|ω_(n)|. The result is a signal having an asymmetricspectrum. Examples of suitable processing methods and filters include,but are not limited to, spectral windowing functions and time domainfunctions which convolve the input signal with a signal whose spectrumis asymmetric. There are a number of techniques that may be used tocreate suitable functions. These techniques include, but are not limitedto, Chebyshev approximation, FIR or IIR filter design, windowingtechniques, recursive design techniques, and inverse Z-transformtechniques.

The band-limited signal shown in FIG. 8A has the power spectrum shown inFIG. 8B. When the filter shown in FIG. 9 is applied to the signal ofFIG. 8A, an analytical signal having the power spectrum shown in FIG. 10is created. That signal then may be sampled at a frequency less thantwice the bandwidth, as shown in FIG. 11A. For an electrocardiogramsignal, by down sampling the signal at T-wave locations, the digitalspectrum is still a periodic spectrum with a period of 1/samplinginterval, i.e., the heart rate. As shown in FIG. 11B, interferencebetween the positive and negative frequencies is eliminated since thenegative part of the spectrum is removed.

This approach allows an accurate measurement of T-wave alternans evenwhen there is colored noise at or close to alternans frequency, such asmay occur during infant motion. FIGS. 12A and 12B illustrate acomparison between the analytical approach and the spectral approach. Itis evident that the presence of colored noise within the noise bandresults in an overestimation of alternans power and underestimation ofnoise power in the spectral approach. By contrast, the analyticalapproach provides an accurate estimation of both the alternans and thenoise within the noise band.

Referring to FIG. 13, in a procedure 1300 for identifying infants atrisk for SIDS, a physician or other operator first places ECG electrodeson the infant (step 1310). For example, seven MICRO-V ALTERNANS SENSORS(Cambridge Heart, Bedford, Mass.) and seven standard electrodes may beplaced in the standard 12-lead configuration, as well as 4 Frank vectorpositions, on the infant. After the electrodes have been applied, theoperator then “stresses” the infant to increase the infant's heart rate(step 1320). Although the infant may not be able to perform commonstress tests like a treadmill stress test, other stress tests such aschanging the infant's position, tickling or pinching the infant,shouting or otherwise startling the infant, administering drugs,removing a parent from the infant's line of sight, feeding the infant,or waiting for a bowel movement by the infant may be used to increasethe heart rate of the infant.

Electrical signals from the electrodes are received during the stresstest using, for example, the HEARTWAVE SYSTEM (Cambridge Heart, Bedford,Mass.) or another ECG system capable of processing the data (step 1330).The received electrical signals are then electronically analyzed toidentify T-wave alternans in the ECG of the infant using, for example, aprogrammable processor (step 1340). Analysis may include performingeither the analytical approach or the spectral approach discussed above.

Next, the QT interval in the infant's ECG is measured (step 1350).T-wave alternans measurement from the patient infant is compared withT-wave alternans in one or more infant populations (step 1360) and theQT interval measurement from the patient infant with QT intervals in oneor more infant populations (step 1370) using, for example, aprogrammable processor acting as a comparator. By performing one or bothof these comparisons, infants at risk for SIDS maybe identified (step1380) so that preventative measures reducing the likelihood of death ofthe infant can be taken.

In one implementation, in order to identify infants at risk for SIDS,the measured alternans of the heart is analyzed and classified. Themeasured alternans may be accessed and automatically analyzed to produceone or more interpretation parameters. The interpretation parameters maybe used to generate interpretation results related to the alternans datato classify the alternans data, and the interpretation results may bemade accessible for examination.

The measured alternans may include data related to a reference signalassociated with a factor that affects the quality of the alternansmeasures or the generation of the alternans. For example, with respectto the quality of the alternans measures, the reference signal mayinclude a signal that masks or mimics the presence of alternans. Thereference signal also may include a measure of noise that exists in thedata. Signals that may affect the generation of alternans include, forexample, a measure of the patient's heart rate or respiratory activity.

The interpretation parameters may include a measure of a highest heartrate in the data or a highest heart rate at which sustained alternans isdefinitely not present. Other examples include a measure of a heart rateabove which sustained alternans exists and below which sustainedalternans does not exist, or an indication of the existence ornon-existence of sustained alternans.

Analyzing the alternans data may include automatically evaluating ameasure of alternans that is indicative of the presence of sustainedalternans. For example, the measure of alternans may include a measureof a voltage or of an area associated with the alternans. Likewise, themeasure of the alternans may include a measure of a power spectrum ofthe alternans or a dynamically estimated magnitude of the alternans,obtained, for example, by complex demodulation of the electrocardiogram.A measure of noise associated with the alternans, e.g., a measure of astandard deviation of the noise, also may be indicative of sustainedalternans. Other examples include measures of a temporal duration of thealternans, of gaps in the alternans, or of a measure of the alternansbased upon evaluation of time reversed alternans data.

Analyzing the alternans data may include, for example, automaticallyusing a first search to search the alternans data for sustainedalternans. After using the first search, a different search also may beused to search the alternans data for sustained alternans. The differentsearch may be used, for example, when the first search does not findsustained alternans in the alternans data or when a determination ismade that the findings of the first search are suspect as a result of apoor quality of the alternans data.

Analyzing the alternans data also may include evaluating the dataprovided by an individual electrocardiogram lead or evaluating acombination of adjacent precordial electrocardiogram leads.

The interpretation results that are generated to classify the measuredalternans may include the interpretation parameter and/or a clinicalinterpretation regarding the existence of sustained alternans in thealternans data. The clinical interpretation may, for example, positivelyindicate the existence of sustained alternans, negatively indicate theexistence of sustained alternans, or indicate that the existence ofsustained alternans is indeterminate.

Using the interpretation parameter to calculate the interpretationresults may include using the interpretation parameter to traverse adecision tree to produce the interpretation results based on thealternans data. Another example includes comparing the interpretationparameter to a heart rate threshold to produce the interpretationresults based on the alternans data.

The interpretation results may be made accessible for examination by,for example, graphically displaying the alternans measure, the referencesignal, and the interpretation results. The interpretation results alsomay be made accessible by storing the alternans measure, the referencesignal, and/or the interpretation results in a human or machine readableformat. In any event, the alternans measure and the reference signal maybe displayed using a common time axis and the interpretation results maybe graphically associated to an associated feature of the alternansmeasure and/or the reference signal. A message describing theinterpretation results also may be included.

The alternans trend report is evaluated by a trained physician, whoassigns a clinical interpretation of “positive,” “negative” or“indeterminate” to the alternans result. The alternans trend data may bedifficult to interpret, especially when the alternans exists in thepresence of noise or abnormal ECG beats. Ultimately, the physician mustexercise subjective judgment based on his or her own experience andtraining to determine whether the alternans is significant andsustained, and to estimate the values of Onset HR (the heart rate at theonset of sustained alternans), Max Neg. HR (the highest heart rate atwhich alternans is definitively not present), and other parameters. Theaccuracy and reliability of the interpretation of the trend datatherefore varies from physician to physician as a function of experienceand training. This inter-reader variability diminishes the predictivevalue of the alternans test and is avoided by the automaticinterpretation.

Further information about the analysis and classification of measuredalternans can be found at U.S. application Ser. No. 09/785,558, filedFeb. 20, 2001, and entitled “AUTOMATED INTERPRETATION OF T-WAVEALTERNANS RESULTS,” the contents of which are incorporated herein byreference.

A process embodying these techniques may be performed by a programmableprocessor executing a program of instructions to perform desiredfunctions by operating on input data and generating appropriate outputdata. The techniques may be implemented in one or more computer programsthat are executable on a programmable system including at least oneprogrammable processor coupled to receive data and instructions from,and to transmit data and instructions to, a data storage system, atleast one input device configured to receive the ECG signals, and atleast one output device. Each computer program may be implemented in ahigh-level procedural or object-oriented programming language, or inassembly or machine language if desired; and in any case, the languagemay be a compiled or interpreted language. Suitable processors include,by way of example, both general and special purpose microprocessors.Generally, a processor will receive instructions and data from aread-only memory and/or a random access memory. Storage devices suitablefor tangibly embodying computer program instructions and data includeall forms of non-volatile memory, including by way of examplesemiconductor memory devices, such as Erasable Programmable Read-OnlyMemory (EPROM), Electrically Erasable Programmable Read-Only Memory(EEPROM), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and Compact DiscRead-Only Memory (CD-ROM). Any of the foregoing may be supplemented by,or incorporated in, specially-designed ASICs (application-specificintegrated circuits).

Other implementations are within the scope of the following claims.

1. A system for identifying infants at risk for SIDS, comprising: aninput unit configured to receive electrical signals from electrodesapplied to an infant; a processor connected to the input unit andconfigured to process the received electrical signals to measurealternans of a heart of the infant; and a comparator configured tocompare the measured alternans with alternans in a population ofinfants.
 2. The system of claim 1 wherein: the processor is configuredto process the received electrical signals to measure T-wave alternansof the heart of the infant; and the comparator is configured to comparethe measured T-wave alternans with T-wave alternans in a population ofinfants.
 3. The system of claim 1 wherein the processor is configured toprocess the received electrical signals to measure a QT interval of theheart of the infant.
 4. The system of claim 3 wherein the comparator isconfigured to compare the measured QT interval with QT intervals in apopulation of infants.
 5. The system of claim 1 wherein the receivedelectrical signals comprise electrocardiogram signals.
 6. The system ofclaim 1 wherein further comprises an analog-to-digital converterconfigured to sample the received electrical signals at a frequency lessthan or equal to twice a frequency corresponding to alternans togenerate sample points.
 7. The system of claim 6 wherein the processoris configured to compute an alternating energy at each of the samplepoints for the series of beats.
 8. The system of claim 7 wherein theprocessor comprises: a matrix constructor configured to construct samplepoint matrices, having rows and columns, from the generated samplepoints; an adder configured to sum the alternating energy over theentire set of sample points to generate a total alternating energy; anda divider configured to normalize the total alternating energy withrespect to an energy of the average waveform.
 9. The system of claim 6,wherein the processor is configured to create a processed signal havingan asymmetric spectrum, and to process the processed signal to generatean indication of alternans in the received electrical signals.
 10. Thesystem of claim 9 wherein the processor is configured to create theprocessed signal as an analytical signal.
 11. The system of claim 10wherein the processor is configured to create the processed signal as ananalytical signal by generating a frequency domain representation of thereceived electrical signals, modifying the frequency domainrepresentation to remove components corresponding to negativefrequencies, and generating the analytical signal as a time domainrepresentation of the modified frequency domain representation.
 12. Thesystem of claim 1 wherein: the input unit comprises circuitry configuredto receive an electrocardiogram signal; and the system further comprisesan electrode connected to the input unit and configured to produce anelectrocardiogram signal.
 13. The system of claim 12 wherein theprocessor is configured to sample the electrocardiogram signal at afrequency of once per beat.